Reinforcement Learning
Worst-Case Regret Bounds for Exploration via Randomized Value Functions
This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
Shakir Mohamed, Danilo Jimenez Rezende
Neural Information Processing Systems http://nips.cc/
Multi-Task Reinforcement Learning with Soft Modularization Ruihan Y ang
Multi-task learning is a very challenging problem in reinforcement learning.
Information-Theoretic Confidence Bounds for Reinforcement Learning
Neural Information Processing Systems http://nips.cc/
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Shuyue Hu, Chin-wing Leung, Ho-fung Leung
Neural Information Processing Systems http://nips.cc/
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Wenhao Yang, Xiang Li, Zhihua Zhang
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term.
AvE: Assistance via Empowerment
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on inferring the human's goal, which is challenging when there are many potential goals or when the set of candidate goals is difficult to identify. We propose a new paradigm for assistance by instead increasing the human's ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment .
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
Neural Information Processing Systems http://nips.cc/
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
Neural Information Processing Systems http://nips.cc/
Reward-rational (implicit) choice: A unifying formalism for reward learning
The types of behavior interpreted as evidence of the reward function have expanded greatly in recent years. We've gone from demonstrations, to comparisons,